Please use this identifier to cite or link to this item: https://doi.org/10.1117/12.813731
Title: Toward translational incremental similarity-based reasoning in breast cancer grading
Authors: Tutac, A.E.
Racoceanu, D.
Leow, W.-K. 
Müller, H.
Putti, T.
Cretu, V.
Keywords: Breast cancer grading
Case-based reasoning
Content-based image retrieval
Histopathology
Incremental learning
Similarity-based reasoning
Translational approach
Issue Date: 2009
Source: Tutac, A.E.,Racoceanu, D.,Leow, W.-K.,Müller, H.,Putti, T.,Cretu, V. (2009). Toward translational incremental similarity-based reasoning in breast cancer grading. Progress in Biomedical Optics and Imaging - Proceedings of SPIE 7260. ScholarBank@NUS Repository. https://doi.org/10.1117/12.813731
Abstract: One of the fundamental issues in bridging the gap between the proliferation of Content-Based Image Retrieval (CBIR) systems in the scientific literature and the deficiency of their usage in medical community is based on the characteristic of CBIR to access information by images or/and text only. Yet, the way physicians are reasoning about patients leads intuitively to a case representation. Hence, a proper solution to overcome this gap is to consider a CBIR approach inspired by Case-Based Reasoning (CBR), which naturally introduces medical knowledge structured by cases. Moreover, in a CBR system, the knowledge is incrementally added and learned. The purpose of this study is to initiate a translational solution from CBIR algorithms to clinical practice, using a CBIR/CBR hybrid approach. Therefore, we advance the idea of a translational incremental similarity-based reasoning (TISBR), using combined CBIR and CBR characteristics: incremental learning of medical knowledge, medical case-based structure of the knowledge (CBR), image usage to retrieve similar cases (CBIR), similarity concept (central for both paradigms). For this purpose, three major axes are explored: the indexing, the cases retrieval and the search refinement, applied to Breast Cancer Grading (BCG), a powerful breast cancer prognosis exam. The effectiveness of this strategy is currently evaluated over cases provided by the Pathology Department of Singapore National University Hospital, for the indexing. With its current accuracy, TISBR launches interesting perspectives for complex reasoning in future medical research, opening the way to a better knowledge traceability and a better acceptance rate of computer-aided diagnosis assistance among practitioners.©2009 SPIE.
Source Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE
URI: http://scholarbank.nus.edu.sg/handle/10635/41673
ISBN: 9780819475114
ISSN: 16057422
DOI: 10.1117/12.813731
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